arXiv Machine Learning

Differentiable Causal Discovery for Singular Linear Models under Confounding

arXiv AI

Jul 13

How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding

arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.

By Debargha Ghosh, Silja Renooij, Anna Kononova

arXiv AI

Aug 6

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery

arXiv:2608. 04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.

By Shrenik Zinage

multimodal benchmarks

arXiv Machine Learning

Jul 28

Amortized Bayesian Causal Discovery of Extended Factor Graphs

arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.

By Yichen Gu, Yuxuan Song, Weizhou Qian, Yixin Wang, Joshua Welch

benchmarks

arXiv Machine Learning

Jun 17

Instrumental and Proximal Causal Inference with Gaussian Processes

arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.

By Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau

arXiv AI

Aug 14

Interpretable Causal Discovery via Causal-Effect Constraints

arXiv:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.

By Cixuan Zhang, Guy Van den Broeck, Benjie Wang

arXiv Machine Learning

1d ago

Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders

arXiv:2609.18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data. In various fields, exploring causal relationships among variables remains an...

By Weijian Yu, Jean Honorio

arXiv Machine Learning

Jul 7

Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets

arXiv:2607. 04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning.

By Seong Woo Ahn, Alessandro Leite, Jos\'e Lucas De Melo Costa, Fabrice Popineau, Bich-Li\^en Doan, Arpad Rimmel

arXiv AI

Jun 2

Transferring Information Across Interventions in Causal Bayesian Optimization

arXiv:2606. 01457v1 Announce Type: new Abstract: Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money.

By Mohammad Ali Javidian

benchmarks

arXiv AI

Jul 14

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.

By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv Machine Learning

Jun 5

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.

By Hazhir Aliahmadi, Irina Babayan, Greg van Anders

arXiv Machine Learning

Jun 26

Use What You Know: Causal Foundation Models with Partial Graphs

arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.

By Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf

arXiv Machine Learning

Jun 19

Unsupervised Causal Abstractions Discovery

arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.

By Th\'eo Saulus, Simon Lacoste-Julien, Dhanya Sridhar

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